SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Showing 30413050 of 3073 papers

TitleStatusHype
Active Learning for Coreference Resolution0
DutchSemCor: Targeting the ideal sense-tagged corpus0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables0
Distribution-Dependent Sample Complexity of Large Margin Learning0
Active learning for interactive machine translation0
Bayesian Active Learning for Classification and Preference LearningCode0
Video Annotation and Tracking with Active Learning0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity0
Lower Bounds for Passive and Active Learning0
Active Learning with a Drifting Distribution0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified